[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290816-105":59,"doc-detail-290816-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","machine-learning-for-wind-speed-estimation","Machine Learning for Wind Speed Estimation","","For more than two decades, computational analysis has expanded architectural capabilities, enabling sustainable design through detailed environmental evaluation. Wind dynamics are central to creating comfortable built environments because they shape pedestrian comfort around buildings. Traditionally, wind tunnel tests, in situ measurements, and CFD simulations estimate urban wind speeds. This study evaluates pedestrian-level wind speeds using machine learning and compares results with traditional in situ measurements and CFD simulations, showing sufficient accuracy for preliminary design and addressing challenges in integrating visual model outputs with quantitative data to improve reliability and applicability.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-for-wind-speed-estimation/290816/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-for-wind-speed-estimation/290816.png","ImageObject",300,407,{"name":92,"@type":93},"Sage","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-06","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",15,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is wind speed important in architectural and smart city contexts?","Question",{"text":113,"@type":114},"Wind speed affects natural ventilation, passive cooling, energy efficiency, and pedestrian comfort, including thermal comfort and pollutant dispersion. Urban planning choices influence wind speed and resulting comfort levels around buildings.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What traditional methods are used to assess wind speeds in urban settings?",{"text":118,"@type":114},"Common approaches include wind tunnel experiments, in situ measurements, and computational fluid dynamics (CFD) simulations.",{"name":120,"@type":111,"acceptedAnswer":121},"How does the study use machine learning for wind speed estimation and what does it compare against?",{"text":122,"@type":114},"The study applies machine learning to predict pedestrian-level wind speeds, then performs a comparative analysis against traditional in situ measurements and CFD simulations. 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Machine Learning for Wind Speed Estimation. Buildings 2025, 15, 1541 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)buildings15091541  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Department of Architecture, Faculty of Art, Design and Architecture, Sakarya University,  \n54050 Sakarya, Türkiye  \n2 Department of Architecture, Faculty of Architecture, Bursa Uluda ˘g University, 16059 Bursa, Türkiye; [miraygur@uludag.edu.tr](miraygur@uludag.edu.tr)  \n* Correspondence: [ikaradag@sakarya.edu.tr](ikaradag@sakarya.edu.tr)  \nAbstract: For more than two decades, computational analysis has been pivotal in expanding architectural capabilities, enabling sustainable design through detailed environmental analysis. Central to creating sustainable environments is the profound understanding of wind dynamics, which significantly influence comfort levels around buildings. Traditionally, wind tunnel experiments, in situ measurements, and computational fluid dynamics (CFD) simulations have been employed to assess wind speeds in urban settings. However, the advent of machine learning (ML) has introduced innovative methodologies that extend beyond these conventional approaches, offering new insights and applications in architectural design. This study focuses on evaluating pedestrian-level wind speeds using ML techniques, with a comparative analysis against traditional in situ measurements and CFD simulations. Our findings reveal that ML can predict wind speeds with sufficient accuracy for preliminary design phases. One of the primary challenges addressed is the integration of visual outputs from ML models with quantitative data, a necessary step to enhance model reliability and applicability. By developing novel techniques for this integration, our research marks a significant contribution to the field, benchmarking the effectiveness of ML against established methods. The results validate the ML model’s capability to accurately estimate wind speeds, thereby supporting the design of more sustainable and comfortable urban environments.  \nKeywords: built environment; environmental impact; neural networks; sustainability; urban simulation  \n1. Introduction  \nSmart cities and architecture are associated with wind measurement due to its contribution to sustainable cities and architecture, cost-effective and health-conscious architectural solutions, and energy efficiency. The potential of this system lies in its ability to decrease energy expenses by utilizing natural ventilation and passive cooling while also ensuring a comfortable urban environment [1,2] . Wind speed is affected by urban planning, which subsequently impacts pedestrian wind comfort, thermal comfort, and emission dispersion [3–10] . Therefore, there is a need for increased research on urban planning and enhanced cooperation between wind engineers, architects, and urban planners [11] . The form and arrangement of buildings need to be considered [7,12–20] as airflow patterns caused by structures can prevent mobility and result in discomfort [1,21] .  \nThe process of integrating wind solutions for pedestrian comfort into cities involves several key stages, such as assessing wind impacts, identifying critical areas and quality standards, and translating desired qualities and large-scale solutions into actionable guidelines. The approaches utilized to assess wind conditions in urban areas can be classified a","cbCaippiEwjqnPHC","https://ap.wps.com/l/cbCaippiEwjqnPHC","pdf",6524989,22,"English","# Abstract\n# Keywords\n# 1. Introduction","[{\"question\":\"Why is wind speed important in architectural and smart city contexts?\",\"answer\":\"Wind speed affects natural ventilation, passive cooling, energy efficiency, and pedestrian comfort, including thermal comfort and pollutant dispersion. Urban planning choices influence wind speed and resulting comfort levels around buildings.\"},{\"question\":\"What traditional methods are used to assess wind speeds in urban settings?\",\"answer\":\"Common approaches include wind tunnel experiments, in situ measurements, and computational fluid dynamics (CFD) simulations.\"},{\"question\":\"How does the study use machine learning for wind speed estimation and what does it compare against?\",\"answer\":\"The study applies machine learning to predict pedestrian-level wind speeds, then performs a comparative analysis against traditional in situ measurements and CFD simulations. It also focuses on integrating ML visual outputs with quantitative data to improve model reliability.\"}]","Machine Learning for Wind Speed Estimation | PDF",55]